The encoder is to map the input story context x into a compact vector that can capture its essential context features.

we use a normal bi-directional LSTM as the encoder.


The decoder aims to generate a story ending which accords with the target sentiment intensity s. As shown in Figure 3, the probability of generating a target word P is composed of two probabilities: where P R (y t ) denotes the semantic generation probability, P S (y t ) denotes the sentiment generation probability, ↵ and are trainable coefficients.

p R (y t ) is defined as follow: where w is a one-hot indicator vector of word w, W R and b R are trainable parameters, h t is the t-th hidden state of the LSTM decoder with attention mechanism (Luong et al., 2015).


Proposed RankQA: This paper proposes a novel paradigm for neural QA.

we augment the conventional two-staged process with an additional third stage for efficient answer reranking.


(2017), although there are methodological differences: the previous authors sum the probability scores for identical answers, whereas the aim in RankQA is to generate a rich set of aggregation features.

we group all answer candidates with an identical answer span.


During our experiments, we measure the endto-end performance of the entire QA pipeline in terms of exact matches.

we count the fraction of questions for which the provided answer matches one of the ground truth answers exactly.


Therefore, we measure the fraction of correctly answered questions out of those questions that had been answered correctly before re-ranking.

we found that the ratio of answers that remained correct varies between 94.6 % and 96.1 %.


However, this gives rise to a noise-information trade-off (Kratzwald and Feuerriegel, 2018).

selecting a large n generates many candidate answers, but increases the probability of selecting the wrong final answer.


2 Details for find op, form subq in Appendix B. Algorithm 1 Sub-questions generation using Given a decomposition, we use a single-hop RC model to answer each sub-question.

the goal is to obtain the answer and the evidence, given the sub-question and N paragraphs.


To deal with ungrammatical questions generated through our decomposition procedure, we augment the training data with ungrammatical samples.

we add noise in the question by randomly dropping tokens with probability of 5%, and replace wh -word into 'the' with probability of 5%.


We collect a new set of distractor paragraphs to evaluate if the models are robust to the change in distractors.

6 we follow the same strategy as the original approach  using TF-IDF similarity between the question and the paragraph, but with no overlapping distractor paragraph with the original distractor paragraphs.


An important aspect of such a scenario is the existence of the entities in a knowledge text which align with at least one of the answer choices and the pronoun to be resolved.


In this paper, we study the problem of multi-hop text-based QA, which requires multi-hop reasoning among evidence scattered around multiple raw documents.

a query utterance and a set of accompanying documents are given, but not all of them are relevant.


From the table we can see that each of our model components can provide from 1% to 2% relative gain over the QA performance.

using a 1-layer fusion block leads to an obvious performance loss, which implies the significance of performing multi-hop reasoning in HotpotQA.


We introduce Dynamically Fused Graph Network (DFGN) to address multi-hop reasoning.

we propose a dynamic fusion reasoning block based on graph neural networks.


In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language.

we propose to use a Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders.


We train the encoder parameters θ on a downstream task via gradient-based optimization.

we train NLPROLOG with backpropagation using a learning from entailment setting (Muggleton and Raedt, 1994), in which the model is trained to decide whether a Prolog program R entails the truth of a candidate triple c ∈ C, where C is the set of candidate triples.


The SEQ2SEQ model 1 https://github.com/lijierui/ group-attention takes the text of the whole problem as the input and corresponding equation as the output.

the group attention consists of four different types of multi-head attention modules.


The key difference between the two expressions is that f I scores the start and the end of each span independently, while f J scores them jointly.

we define f J : R h → R and f I : R h → R to be transformations of the final token representations computed by a BERT model: Here h is the hidden representation dimension, (s, e) = a is the answer span, BERT(t)[i] is the BERT representation of the i'th token in token sequence t. MLP J is a multi-layer perceptron with a single hidden layer, and AFF I is an affine transformation.


The final step in the argument is to make the case that the algorithms described in the current paper may effectively be optimizing a criterion of this kind.

the auxiliary function β(θ A ) is defined as the log-likelihood of noisy (c, q, a) triples generated from unlabeled data using the C → A and C, A → Q models; constraining the parameters θ A to achieve a relatively high value on β(θ A ) is achieved by pre-training the model on these examples.


In this work, we present novel approaches to exploit sentential context for neural machine translation (NMT).

we first show that a shallow sentential context extracted from the top encoder layer only, can improve translation performance via contextualizing the encoding representations of individual words.


The source-side context vector is fed to the decoder, so that translation at each step is conditioned on the whole source-side context.

we propose two types of sentential context: 1) the shallow one that only exploits the top encoder layer, and 2) the deep one that aggregates the sentence representations of all the encoder layers.


In this work, we propose to exploit sentential context for neural machine translation.

the shallow and the deep strategies exploit the top encoder layer and all the encoder layers, respectively.


Further, this trend is seen in the newspapers' relative rates: being more oriented towards business topics and targeting an educated audience, The Guardian features less code-switching in its comment sections compared to The Punch, a tabloid with a wider audience (Marcus, 1999).

the code switching effect is strongest for topics that relate to societal tensions (e.g., political, socioeconomic, and tribal).


In contrast to skip-thought vectors (Kiros et al., 2015) which are learned by reconstructing the surrounding sentences of an encoded sentence, we borrow the idea of negative sampling from word representation learning (Mikolov et al., 2013).

for a sentence s i in document D, we take its previous sentence s i−1 and its following sentence s i+1 to be positive examples, and consider any other sentence in the corpus to be a negative example.


The second block in Table 2 presents the results of the LEAD-3 baseline (which simply creates a summary by selecting the first three sentences in a document) as well as various instantiations of 61.9 41.7 58.3 54.7 30.4 50.8 REFRESH 4 (Narayan et al., 2018b) 41.3 22.0 37.8 41.3 18.4 37.5 POINTER-GENERATOR (See et al., 2017)   TEXTRANK (Mihalcea and Tarau, 2004).

we experimented with three sentence representations to compute sentence similarity.


In addition to automatic evaluation using ROUGE, we also evaluated system output by eliciting human judgments.

we assessed the degree to which our model retains key information from the document following a questionanswering (QA) paradigm which has been previously used to evaluate summary quality and document compression (Clarke and Lapata, 2010;Narayan et al., 2018b).


A.1 Examples of System Output Table 6 shows examples of system output.

we show summaries produced from GOLD, LEAD, TEXTRANK and PACSUM for test documents in NYT, CNN/Daily Mail and TTNews.


Our work departs from previous generalpurpose semantic parsers (Flanigan et al., 2016;Foland and Martin, 2017;Lyu and Titov, 2018;Liu et al., 2018;van Noord et al., 2018b) in that we focus on building representations for entire documents rather than isolated utterances, and introduce a novel semantic parsing task based on DRT.

our model operates over Discourse Representation Tree Structures (DRTSs) which are DRSs rendered in a tree-style format (Liu et al.


For this reason, we propose a method to explicitly learn this alignment by exploiting the feature representations afforded by multi-attention.

we obtain alignment weights via multi-attention: where β align km = P (a k = m|h y k , [h x 0 : h xm ]), i.e., the probabilistic distribution over alignments from sentences to the kth prediction in the decoder, where a k = m denotes the kth prediction aligned to the mth sentence.


Copying from Alignments We use alignment as a means to modulate which information is copied.

we allow copying to take place only over sentences aligned to elementary DRS nodes.


Table 5, we examine whether constraint-based inference is helpful.

we show the percentage of ill-formed DRTSs when constraints are not enforced.


In this paper, we apply the divide-and-conquer philosophy to matching a pair of articles and bring deep text understanding from the currently dominating sequential modeling of language elements to a new level of graphical document representation, which is more suitable for longer articles.

we have made the following contributions: First, we propose the so-called Concept Interaction Graph (CIG) to represent a document as a weighted graph of concepts, where each concept vertex is either a keyword or a set of tightly connected keywords.


This way, the matching of two documents is converted to match the pair of sentence sets on each vertex of G AB .

we generate local matching vectors based on both neural networks and term-based techniques.


Fortunately, this will come naturally to researchers in the field as they are already trained to avoid features that encode topic rather than style.

we strongly suggest that future evaluations should adopt a stateless one-case-at-a-time test policy.


In our task setting, we classify numerals, denoted as m, into eight classes by their magnitudes, as listed in Table 3.

we will examine whether NN models can insert a proper range of numerals into a market comment, rather than inserting the exact number.


(E1) indicates the problem of a different contract for the same financial instrument.

the government may publish the same bond with a different coupon rate.


Our proposed sentence content task is a paragraph-level analogue to the word content task of Adi et al.

(2017): given embeddings 2 p, s of a paragraph p and a candidate sentence s, respectively, we train a classifier to predict whether or not s occurs in p. we construct a binary classification task in which the input is [p; s], the concatenation of p and s. This task is balanced: for each paragraph p in our corpus, we create one positive instance by sampling a sentence s + from p and one negative instance by randomly sampling a sentence s − from another paragraph p .


With fine-tuning, CNN-SC substantially boosts accuracy and generalization We switch gears  (Zhang et al., 2017) 95.4 98.2 72.6 pre-training + fine-tuning w/o external data CNN-R (Zhang et al., 2017) 96.0 98.8 96.6 99.0 74.9 pre-training + fine-tuning w/ external data ULMFiT (Howard and Ruder, 2018)   now to our fine-tuning experiments.

we take the CNN encoder pre-trained using our sentence content objective and then fine-tune it on downstream classification tasks with supervised labels.


A.1 BoW models outperform more complex models on our sentence content probe In addition to the paragraph embedding models presented in the main paper, we also experiment  with the following embedding methods: We consider an LSTM (Hochreiter and Schmidhuber, 1997) encoder-decoder model paired with a reconstruction objective.

we implement a single-layer bidirectional LSTM encoder and a two-layer unidirectional LSTM decoder.


One of the key discoveries of this paper is that adding noise to the document-topic vectors during training leads to substantially better topics.

for each training example we sample a random Dirichlet vector from the prior θ noise ∼ P Θ and mix with the encoder output θ = enc(w): where α ∈ [0, 1] is the mixing proportion.


We can extract the top words based on the decoder matrix weights.

the representative words of the kth topic are those corresponding to the top entries of β k sorted in descending order.


Functions , and f π (•) are implemented as neural networks whose parameters are collectively denoted by θ.

implementing f π (•) is as simple as augmenting a standard NMT architecture (Bahdanau et al., 2015;Luong et al., 2015), i.e.


This evidence lowerbound (ELBO) is expressed in terms of an inference model q λ (z|x m 1 , y n 1 , v) which we design having tractability in mind.

our ap-3 This is an aspect of the model we aim to explore more explicitly in the near future.


Architecture All of our parametric functions are neural network architectures.

f π is a standard sequence-to-sequence architecture with attention and a softmax output.


However, keeping in mind that MMT datasets are rather small, it is desirable to simplify some of our model's components.

the estimated latent Gaussian model (1) can be replaced by a fixed standard Gaussian prior, i.e., Z ∼ N (0, I)-we refer to this model as VMMT F .


In addition to human action recognition, our work relates to other multimodal tasks such as visual question answering (Jang et al., 2017;Wu et al., 2017), video summarization (Gygli et al., 2014;Song et al., 2015), and mapping text descriptions to video content (Karpathy and Fei-Fei, 2015;Rohrbach et al., 2016).

we use an architecture similar to (Jang et al., 2017), where an LSTM (Hochreiter and Schmidhuber, 1997) is used together with frame-level visual features such as Inception (Szegedy et al., 2016), and sequence-level features such as C3D (Tran et al., 2015).


Moreover, the addition of extra information improves the results for both modalities.

the addition of context is found to bring improve-  Table 6: Results from baselines and our best multimodal method on validation and test data.


In the context of machine learning, inductive bias can instead be injected via multi-task learning, where supervision from the secondary task guides the model towards appropriately biased representations.

our motivation for the SPEECH/TEXT task is to encourage the model to learn speech representations which are correlated with the encoding of spoken language as a sequence of characters.


Compared to image features extracted from VGG-Net (Simonyan and Zisserman, 2014) and ResNet , this type of features from Faster R-CNN has achieved state-of-the-art performance in both image captioning and VQA  tasks.

the image features F I for a raw image I are represented by: where M = 36 is the number of detected objects in an image 1 , and n f = 2048 is the dimension of the feature vector.


Then, a self-attention mechanism is applied to learn the attention weight of every word in the snippet, identifying the key words and ruling out irrelevant information.

where ω j ∈ R 1×K , p ω ∈ R n h ×1 , W h ∈ R n h ×n h , and u j ∈ R 1×n h .


Now, we use Multimodal Factorized Bilinear pooling (MFB) (Yu et al., 2017c) to fuse v t and d t together.

where The function SumPooling(x, k) in (6) means using a onedimensional non-overlapped window with the size k to perform sum pooling over x.


Two different rank aggregation methods are explored here: (i) average over ranks; and (ii) average over reciprocal ranks.


Dataset We evaluate our proposed approach on the recently released VisDial v1.0 dataset 3 .

the training and validation splits from v0.9 are combined together to form the new training data in v1.0, which contains dialogs on 123, 287 images from COCO dataset (Lin et al., 2014).


For any two nodes i, j in the lattice graph, the matrix entry L ij is the minimum relative distance between them.

if the nodes i, j share at least one path, then we have where L p •0 is the distance to the source node in path p. If no common path exists for two nodes, we denote the relative distance as −∞ (−10 9 in practice) for subsequent masking in the lattice attention.


In this work, we propose a goal-driven collaborative task that combines language, perception, and action.

we develop a Collaborative image-Drawing game between two agents, called CoDraw.


(2015), we only consider a list of words with high frequency in the dataset as seeds, denoted as H. The relevance distribution of words in H for an image I i is computed as follows: is in the same size as H. In the second step, we compute the relevance scores of words in the full vocabulary V given the image I i and the probabilities of directly-related words S (H) i .

a 2-layer MLP with sigmoid function is employed.


It contains an additional sublayer which performs multi-head attention over the outputs of the encoder block.

decoding layer d l i is the result of a) multi-head attention over the outputs of the encoder which in turn is a function of the encoder memory and the outputs from the previous layer: where, the keys and values are the encoder outputs and the queries correspond to the decoder input, and b) the multi-head self attention which is a function of the generated outputs from the previous layer: Deliberation networks Xia et al., 2017) build on the standard sequence to sequence architecture to add an additional decoder block (in our case, with 3 layers -see Figure 2).


At the high level, MulT merges multimodal timeseries via a feed-forward fusion process from multiple directional pairwise crossmodal transformers.

each crossmodal transformer (introduced in Section 3.2) serves to repeatedly reinforce a target modality with the low-level features from another source modality by learning the attention across the two modalities' features.


We define the Querys as The latent adaptation from β to α is presented as the crossmodal attention ( 1)Note that Y α has the same length as Q α (i.e., T α ), but is meanwhile represented in the feature space of V β .

the scaled (by √ d k ) softmax in Equation (1) computes a score matrix softmax (•) ∈ R Tα×T β , whose (i, j)-th entry measures the attention given by the i-th time step of modality α to the j-th time step of modality β.


To compare the models comprehensively, we adapt the connectionist temporal classification (CTC) (Graves et al., 2006) method to the prior approaches (e.g., EF-LSTM, MCTN, RAVEN) that cannot be applied directly to the unaligned setting.

these models train to optimize the CTC alignment  objective and the human multimodal objective simultaneously.


Finally, we study the importance of individual crossmodal transformers according to the target modalities (i.e., using Table 4, we find crossmodal attention modules consistently improve over the late-and earlyfusion transformer models in most metrics on unaligned CMU-MOSEI.

among the three crossmodal transformers, the one where language(L) is the target modality works best.


A purely attention-based transformer network is order-invariant.

permuting the order of an input sequence does not change transformer's behavior or alter its output.


Following (Vaswani et al., 2017), we encode the positional information of a sequence of length T via the sin and cos functions with frequencies dictated by the feature index.

we define the positional embedding (PE) of a sequence X ∈ R T ×d (where T is length) as a matrix where: for i = 1, .


Temporal Classification (CTC) (Graves et al., 2006) was first proposed for unsupervised Speech to Text alignment.

cTc is often combined with the output of recurrent neural network, which enables the model to train end-to-end and simultaneously infer speech-text alignment without supervision.


The complex structures between and within sections of the reports pose a great challenge to the automatic report generation.

the section Impression is a diagnostic summarization over the section Findings; and the appearance of normality dominates each section over that of abnormality.


Second, instead of encoding previous sentences from scratch, GSE dynamically encodes GS n based on GS n−1 .

we propose a single layer LSTM with soft-attention (Xu et al., 2015) as GSE.


After examining an area, Planner (PL) determines: 1) whether to terminate the generation process; 2) which writer should generate the next sentence.

besides the shared Global State Encoder (GSE), the rest part of PL is modeled by a two-layer feed-forward network: where W 1 , W 2 , and W 3 are parameter matrices; idx n ∈ {0, 1, 2} denotes the indicator, where 0 is for STOP, 1 for NW and 2 for AW.


Consequently, we construct baselines relying solely on captions and ground truth bounding box locations that are comparable to our task.

running the Stanford Scene Graph Parser (Schuster et al., 2015) on ground truth captions constructs a scene graph just from the image captions (which almost never capture all the information present in an image).


Few of the published articles on regular approximation have discussed the application in practice.

little attention has been given to the following two questions: First, what happens when a context-free grammar grows in size?


Second, how "precise" are the approximations?

how much larger than the original context-free language is the language obtained by a superset approximation, and how much smaller is the language obtained by a subset approximation?


This incarnation then only pertains to the set of rules of the form B --* c~, where B E Ni.

nonterminals not in Ni are treated by this incarnation of the approximation process as if they were terminals.


We now approximate the set of strings derivable from A by G~, which is the set of strings consisting of terminals from GA. Our implementation is made slightly more sophisticated by taking ~A to be {X ] 3B, c~,fl[B E Ni A B ~ oLXfl A X ~ Ni]}, for each A such that A E Ni and recursive(Ni) = self, for some i.

each X E ~A is a terminal, or a nonterminal not in the same set Ni as A, but immediately reachable from set Ni, through B E Ni.


For j = 3,4, 5, however, the approximating language becomes substantially smaller than that in the case of RTN, but at the expense of large automata.

if we compare the sizes of the automata for RTN+j in Figure 16 with those for RTNd in Figures 13 and 14, then Figure 17 suggests the large sizes of the automata for RTN+j are not compensated adequately by a reduction of the percentage of sentences that are recognized.


The resulting finite-state machines are more expressive than standard left-to-right transducers.

they allow long-distance movement with fewer states than a traditional finite-state transducer, a useful property for the translation task to which we apply them in this paper.


The dependency transduction model produces synchronized dependency trees in which each local tree is produced by a head transducer.

the dependency model applies the head transducers recursively, imposing a recursive decomposition of the source and target strings.


Head transducers and dependency transduction models are thus related as follows: Each pair of local trees produced by a dependency transduction derivation is the result of a head transducer derivation.

the input to such a head transducer is the string corresponding to the flattened local source dependency tree.


Similarly, the output of the head transducer derivation is the string corresponding to the flattened local target dependency tree.

the head transducer is used to convert a sequence consisting of a headword w and its left and right dependent words to a sequence consisting of a target word v and its left and right dependent words (Figure 5).


The main transitions that are traced in our construction are those that map heads, In order to generalize from instances in the training data, some model states arising from different training instances are shared.

in the construction described here, for a given pair (w, v) there is only one final state.


Another is that the compilation of this decomposition into lexically anchored finite-state head transducers produces implementations that are much more efficient than those for the IBM model.

our search algorithm finds optimal transductions of test sentences in less than "real time" on a 300MHz processor, that is, the time to translate an utterance is less than the time taken to speak it, an important consideration for our speech translation application.


In order to implement the algorithms efficiently in Prolog, it is important to use efficient data structures.

we use an implementation of (non-updatable) arrays based on the N+K trees of O'Keefe (1990,(142)(143)(144)(145) with N = 95 and K = 32.


The formalism of the framework is to handle various linguistic theories and models including McCarthy's initial findings as well as later models for Hebrew (Bat-E1 1989), moraic theory Prince 1990a, 1995), the affixational approach to handling templates in Arabic (McCarthy 1993), and others.

a flexible formalism that leaves the grammar writer with ample room to choose the appropriate linguistic theory for an application.


Overall Goal: #<KNOW (HEARER, WEATHER-INF0) > Achieve (I) by sequencing information based on the #<SEQUENCE> of the three days involved Support (2) with information about #<SEPT-25> Support (2) with information about #<SEPT-26> Support (2) with information about #<SEPT-27> Requests to express the information about each day's weather are then passed on to the linguistic component.

the planner carries out step (4) of the plan by placing in the workspace the request: A separate workspace entry is created for each node and link in this request, 22 with the entry for the top-level #<0VER-TIME-SPAN> link marked as their "head" entry.


Since the usage of #<w: IT> here is an idiomatic construction, it has some special handling in the lexicon to indicate that its meaning depends on what it is being used to describe.

#<w: IT> here is taken to mean #<TEMPERATURE>.


The discussion of the examples in Section 4.3 glosses over an important detail: IGEN will only generate the output shown there if the time pressure is eased by increasing the delay thresholds.

the choice of #<w :DRIZZLES> and #<w: POURS> over #<w:RAINS> in those examples is made with the thresholds set to force the utterer to always wait at least five time units before uttering the next piece of text.


On the other hand, it's not completely bizarre; it's still talking about the weather, and in fact it's even still describing the precipitation.

iGEN still has a vague sense of what it's trying to say; it's not simply producing an utterance at random.


However, the pronoun it has two possible antecedents, and conceptual knowledge is required to select the appropriate one.

the knowledge that a man can wash objects with water, and that water is carried in buckets, must be employed.


However, when one is instructed to interpret a multimodal message, like Figures 1 and 2, not all information in the scheme of Figure 3 is available.

the translation functions PL-G and PG-L of the graphical and linguistic individual constants mentioned in the texts and the pictures of the multimodal messages are not known, and the crucial inference of the interpretation process has as its goal to find out the definition of these functions (i.e., to establish the relations between names of L and G).


Language L is a segment of English designed to produce expressions useful for referring to objects, properties, and relations commonly found in discourse about maps.

the natural language expression of Figure 2 can be constructed in a compositional fashion.


So, whether it is possible to compute the set of models of a given drawing is an empirical question.

reiter's system employs a constraint satisfaction algorithm to find all possible interpretations of maps, and the output of his system is a set of labels for such as "river", "road", or "shore" for curves or chains, and "land region" or "water region" for areas.


In these structures, DRS conditions extracted from different modalities would be kept in separate partitions, but discourse referents would be abstract objects common to the whole MDRS.

mDRS's could help to specify accessibility relations between anaphoric and deictic terms and their antecedents and interpretation context, imposing severe constraints on the possible interpretations, as is normal in DRT.


D&N's pattern matching (when building the pronunciation lattice) is a "partial" one.

as explained in section 3.1.1, the process starts with the leftmost letter of the input string and of the current dictionary entry aligned and continues until the two are right-aligned.


There seems to be no essential reason for starting and discontinuing matching at these points.

we could shift and match over the range of all possible overlaps--starting with the final e of blope aligned with the initial s of sloping, and terminating with the initial b of the former aligned with the final g of the latter.


To make the translation model estimation methods presented here as general as possible, I shall assume a totally uninformative concept representation--the trans distribution itself.

i shall assume that each different pair of word sequence types is deterministically generated from a different concept, so that trans(.1i,~i]C) is zero for all concepts except one.


However, Equation 26 is a sum rather than a product, so I scale the trans parameters logarithmically, to be consistent with its probabilistic interpretation: Yarowsky (1993, 271) has shown that "for several definitions of sense and collocation, an ambiguous word has only one sense in a given collocation with a probability of 90-99%."

a single contextual clue can be a highly reliable indicator of a word's sense.


All classification was performed by table lookup; no context-aware part-of-speech tagger was used.

words that were ambiguous between open classes and closed classes were always deemed to be in the closed class.


The output was the model's single best guess about the translation of each word in the input, together with the input word.

each model produced link tokens consisting of input words and their translations.


A lexicon entry (u,v) was considered correct if u and v ever appeared as direct translations of each other in an aligned segment pair.

a link type was considered correct if any of its tokens were correct.


This leads to the question of how modules should interact.

is it acceptable to arrange modules in a simple pipeline, where a later module cannot affect an earlier module?


In part this was because updating the size estimator in some cases required considerably more work than making the actual bug fix or improvement, and the developers had many urgent changes that needed to be made to the core software in this period.

another difficulty with building an estimator that predicted the behavior of the microplanner, realizer, and Word was that it was difficult and timeconsuming to maintain the accuracy of the estimator as changes were made to the microplanner and realizer, and also to the exact RTF structures produced by our system for Word to process.


Without reliable global measures of readability, perhaps the best we can do (and the approach adopted in STOP) is to design messages that readability experts think are appropriate for poor readers; this is something that can be done in a single-solution pipeline architecture.

if we cannot properly measure the thing we are trying to optimize or satisfy (which may be the case with the majority of constraints and optimizations that today's NLG systems builders are concerned with), then there may be little value in shifting to a complex architecture that supports more sophisticated search (which is perhaps the main benefit of revision and multiple-solution pipelines).


One of my colleagues, a computational chemist with a background in statistical physics, recently became interested in applying methods from statistical NLP to protein modeling.

* we briefly discussed the notion of using probabilistic contextfree grammars for modeling long-distance dependencies.


In Chapter 11, Roche expands on the part-of-speech-tagging and parsing articles of Roche and Schabes (1997).

he details a finite-state method for syntactically parsing light verbs.


Submissions may be made in any of the following categories: Papers: This category includes contributions that report significant new research results in computational linguistics or that provide critical reviews of the literature on a particular topic.

the length of a paper depends on its content, but it is suggested that manuscripts be limited to forty double-spaced pages.-.the equivalent of thirty journal pages.


How do changes in the W/C-ratio affect G 2 and Fisher's exact test, when applied to contingency tables (a) to (f)?

how does the choice of the window size affect whether a low-frequency word is judged to be a significant term, for fixed A and B (e.g., A = 1 and B = 0 for a hapax legomenon)?


Our conclusion is that, with the W/C-ratio as the only determinant of significance, the windowing method is not powerful enough to distinguish between relevant and irrelevant hapax legomena.

hapax legomena should be removed from consideration a priori.


Returning to the prior distribution of DA sequences P(U), it is convenient to make certain independence assumptions here, too.

we assume that the prior distribution of U is Markovian, i.e., that each Ui depends only on a fixed number k of preceding DA labels: (k is the order of the Markov process describing U).


We estimate the distribution of likely DA types for a given utterance using the entire conversation E as evidence, and then use a sentence-level mixture (Iyer, Ostendorf, and Rohlicek 1994) of DA-specific LMs in a single recognizer run.

we replace P(WilUi) in Equation 11 with ~_~ P(WilUi)P(Ui]E), ui a weighted mixture of all DA-specific LMs.


• Connectives can signal more than one rhetorical relation.

there is no one-to-one mapping between the use of connectives and the rhetorical relations that they signal.


The algorithm presented in this paper assumes that the rhetorical structure of a text correlates with the orthographic layout of that text.

it assumes that sentences, paragraphs, and sections correspond to hierarchical spans in the rhetorical representation of the text that they subsume.


By considering only cue phrases having a discourse function in most of the cases, I deliberately chose to focus more on precision than on recall with respect to the task of identifying the elementary units of text.

i chose to determine fewer units than humans do, hoping that, in this way, most of the identified units would be correct.


The names and the intended semantics of the actions used by the shallow analyzer are: • Action NOTHING instructs the shallow analyzer to treat the cue phrase under consideration as a simple word.

no textual unit boundary is normally set when a cue phrase associated with such an action is processed.


29rhet_reI (ELABORATION,3,[1,2]) rhet_rel (ELABORATION,[4,6], 3) Once the elementary units of a text have been determined and the rhetorical relations between them have been hypothesized at sentence, paragraph, and section levels, we need to determine the rhetorical structures that are consistent with these hypotheses and with the constraints specific to valid RS-trees.

we need to solve the problem of rhetorical structure derivation.


We use operators E~ and \~ instead of E and \ because we treat each exclusive disjunction as a whole because each exclusive disjunction was hypothesized using one and only one hypothesis trigger (cue phrase).

we say that a rhetorical relation r E~ ri • ri+l G • .. • ri+j if r matches any of the rhetorical relations ri .


As I have shown in Marcu (2000), the proof-theoretic account outlined here is both sound and complete with respect to the constraints that characterize the valid rhetorical structures enumerated in Section 2.4.

all theorems that are derived using the disjunctive proof-theoretic account correspond to valid text structures; and  The valid rhetorical structure of text (19); see the relation in (27).


The merging process is a trivial procedure that assembles the trees obtained at each level of granularity.

the trees that correspond to the sentence level are substituted for the leaves of the structures built at the paragraph level, and the trees that correspond to the paragraph levels are substituted for the leaves of the structures built at the section level.


To enable the reader to distinguish between rhetorical and nuclearity errors, I follow the same strategy as in the case of computing labeled recall and precision figures.

i consider by convention that the nuclei nodes of a rhetorical representation are labeled with the relation SPAN.


(1992) report on a discourse analyzer for Japanese, which differs from mine in a number of ways.

important is the fact that the theoretical foundations of Sumita et al.


Recently, syntactic information has also been applied to authorship attribution.

baayen, Van Halteren, and Tweedie (1996) used frequencies of occurrence of rewrite rules as they appear in a syntactically annotated corpus and proved that they perform better than word frequencies.


Moreover, tool-specific knowledge, rather than languagespecific knowledge, is required for the definition of such measures.

researchers using this approach can define analysis-level measures based on their deep understanding of a particular NLP tool even if they are not familiar with the natural language to which the methodology is to be applied.


To this end, we included measures relevant to the actual output of this tool as well as measures relevant to the methodology used by the SCBD to analyze the text.

the proposed set of style markers comprises three levels: • Token Level: The sample text is considered as a set of tokens grouped in sentences.


Moreover, multiple regression can also be used for the estimation of the significance of the independent variables.

the amount by which R 2 is reduced if a certain independent variable is deleted from the regression equation (in other words, the contribution of the independent variable to R 2) is represented by the squared semipartial correlation sri 2 (Tabachnick and Fidell 1996): where ti is the value of the t statistic for the ith variable and dffr¢~ are the residual degrees of freedom.


• Whole text: Neither text length limitations nor other manual text-sampling restrictions are set.

a text has to be available as it appears in its source.


In authorship attribution experiments we chose to deal with texts taken from newspapers, since a wide variety of authors frequently publish their writings in the press, making the collection of a considerable number of texts for several authors easier.

the corpus used for this study comprises texts downloaded from the WWW site of the Modern Greek weekly newspaper TO BHMA, (the tribune).2 The structure of this newspaper is shown in Table 4.


To this end, we performed experiments on text genre detection using multiple regression based on variable training data.

we varied the training corpus, including 7 to 15 texts for each genre, but used the same test corpus of 10 texts for all of the experiments.


the SCBD, we created an artificial defect in the output of the SCBD by corrupting the sentence and chunk boundary detection procedures.

: in the sentence boundary detection procedure, only periods were considered to denote a potential sentence boundary, and the fifth parsing pass was excluded from the chunk boundary detection procedure.


We can implement a simple Russellian theory of definite descriptions by means of the following equivalence: Clearly, possibleAntecedent will be largely the same for definites as for pronouns, although there will have to be provision for inferred antecedents (a car.., the steering wheel).

we need to be able to create discourse entities for quantified antecedents, plurals, etc.


The QLF-based theory illustrated in the approach advocated here does not suffer from these problems: QLFs contain only information for which there is a direct syntactic or morphological reflex.


If we only consider indefinites as potential antecedents, recall is extremely low (12%); we also get the worst precision.

considering only indefinites for the resolution of definite descriptions is too restrictive; this is because our corpus contains a large number of first-mention definite descriptions that serve as antecedents for subsequent references (similar results were also reported in Fraurud [1990]).


The results with this configuration were: • R = 0.75, P = 0.87, F = 0.80, for the training data (compared with R = 0.76, P = 0.88, F = 0.81) ; • R = 0.59, P = 0.83, F = 0.69, for the test data (compared with R = 0.62, P = 0.83, F = 0.71).

the results were about the same, although a slightly better performance was obtained when the tests to identify discourse-new descriptions were tried first.


We define fundamental aspectual class as the aspectual class of a clause before any aspectual transformations or coercions.

the fundamental aspectual category is the category the clause would have if it were stripped of any and all aspectual markers that induce an aspectual transformation, as well as all components of the clause's pragmatic context that induce a transformation.


Therefore, as with most statistical methods for natural language, the linguistic constraints associated with markers are best exploited by a system that measures cooccurrence frequencies.

we measure the frequencies of aspectual markers across verbs.


This technique determines a set of weights for a linear model, which are applied in combination with a certain nonlinear model.

the iterative reweighted least squares algorithm (Baker and Nelder 1989) is employed, and the inverse logic (nonlinear) function is applied.


This representation enables complex interactions between indicator values.

if one indicator can only help classify a proper subset of verbs, while another applies only to a subset that is distinct but intersects with the first, certain ranges of indicator values may delimit verb groups for which the indicators complement one another.


Cross-checking between linguists shows high agreement.

in a pilot study manually annotating 89 clauses from the corpus of novels, two linguists agreed 81 times (i.e., 91%).


As shown above, learning methods successfully produced models that were specialized for the classification task.

the same set of 14 indicators were combined in different ways, successfully improving classification performance for both stativity and completedness, and revealing linguistic insights for each.


For example, GP results for both classification tasks incorporated a subset of only five indicators each.

manner adverb, which ranked highest by logistic regression, is not incorporated in the example function tree induced by GP.


The paper discusses a number of problems with these annotations and concludes that rethinking of the coreference task is needed before the task is expanded.

it suggests a division of labor whereby annotation of the coreference relation proper is separated from other tasks such as annotation of bound anaphora and of the relation between a subject and a predicative NP.


In the following example, the first occurrence of aluminum is only considered to be markable because it corefers with the occurrence of this noun  The price of aluminum siding has steadily increased, as the market for aluminum reacts to the strike in Chile.

(Hirschman and Chinchor 1997) : coreference (step 2) helps to determine what the markables are (step 1).


where 0 = np, n is the average length of a document, and p is the occurrence probability of the term.

The rest of the paper is divided into two sections.


A substring, sub (i,j), is a prefix of a suffix.

sub(i,j), is the first j characters of the suffix s [i].


Moreover, the number of substrings in a nontrivial class depends on the amount of room between the LBL and the SIL.

[class((i,j) Figure 4 shows eight examples of lcp-delirnited intervals.


The interval {10, 11 / is lcp-delimited because the bounding lcps, Icp[lO] --= 0 and lcp [12] = 1, are smaller than the interior lcp, lcp[11] = 4.

the LBL (= 1) is less than the SIL (= 4).


Every substring in the class starts every suffix in the interval (10,11), and no suffix outside (10,11).

the substring "o" is excluded from the class, because it is shared by suffixes outside the interval, namely s[12] and s [13].


Because a bounding lcp of /a, b) must be within (c, d) and an interior lcp of (a, b) must be a bounding lcp of (c,d), SIL ((c,d)) <_ LBL((a,b)) <_ SIL((a,b)) <_ LBL ((c,d)).

the overlapped interval (c, d) is not lcp-delimited.


The substrings in a class all have the same tf value (property 1) and the same df value (property 2).

if Sl and s2 are two substrings in class((i,j)) then Property 1: tf(sl) = tf(s2) = j -i + 1 Property 2: dr(s1) = rid(s2 ).


In the end we discover that df("o") --3.


In both cases, shorter substrings have more dynamic range.

rIDF and MI vary more for bigrams than million-grams.


Topic segmentation is the task of automatically dividing a stream of text or speech into topically homogeneous blocks.

given a sequence of (written or spoken) words, the aim of topic segmentation is to find the boundaries where topics change.


We augmented the Dragon segmenter with additional states and transitions to also capture lexical discourse cues.

we wanted to model the initial and final sentences in each topic segment, as these often contain formulaic phrases and keywords used by broadcast speakers (From Washington, this is .... And now ... ).


For the PAUSE condition, we empirically determined an optimal minimum pause duration threshold to use.

we considered pauses exceeding 0.575 of a second as potential topic boundaries in this (and all later) experiments.


However, for short pauses, the tree queries further prosodic features to prevent false alarms.

short pauses must be accompanied both by an F0 close to the speaker's baseline and by a large F0 reset to be deemed topic boundaries.


The improvement obtained with source-dependent topic switch penalties and posterior thresholds suggests that more comprehensive source-dependent modeling would be beneficial.

both prosodic and lexical discourse cues are likely to be somewhat source specific (e.g., because of different show formats and different speakers).


we claim that the following two aspects of LTAG appear unnecessarily restrictive from the perspective of tree description: .


These three predicate symbols are intended to be interpreted as the immediate domination, domination, and precedence relations, respectively.

in a tree model, the literal x/~ y would be interpreted as node (referred to by the variable) x immediately dominates node (referred to by) y, the literal x/~ y would be interpreted such that x dominates y, and x -~ y indicates that x is to the left of y.


Given a d-tree d, we say that a pair of nodes, x and y, are related by a d-edge if it is known from d that x dominates y, it is not known from d that x immediately dominates y, and there is no other variable in d that is known to be between them.

a pair of nodes x and y, x ~ y, are related byad-edgeifd ~ x~y,d ~ xGy, and for allz E vars(d),ifd ~ (x&zAz~y) then z = x or z = y.


Also, by definition, components must be fully specified with respect to precedence.

for any two nodes u and v within a component we must know whether u precedes v or vice versa.


Graphically, this restriction can be characterized as follows: Recall that not every set of positive literals involving A, /~, and -~ is a legal d-tree.

we can show that a description is a d-tree if and only if it is logically equivalent to descriptions that, when written graphically, would have the appearance described above.


However, when y is not the root of a component, and z is the root of the component containing y, then the tree we obtain from the reading off process is one where x dominates z and not where z properly dominates x.

in this case, we replace the d-edge between x and y with a d-edge between x and z, which we then eliminate in the reading off process by equating x and z.


All the structural components projected from one lexical item (corresponding to the auxiliary tree used in an adjoining step) are included entirely between two components in the other projected structure.

components of only one of the lexically projected structures can get separated in an adjoining step.


We take this example to show once more that TAG imposes restrictions on the linguistic analyses that can be expressed in it.

there are constructions (which do not involve long-distance phenomena) for which one of the most widely developed and comprehensive theories for determining the nature of localization in elementary trees--that of Frank (1992)---calmot be used because of the nature of the TAG operation of adjunction.


Despite its size and all the work that went into it, LGSWE is not a replacement for CGEL but rather a complement to it; indeed, the authors explicitly describe it as such (p. viii).

the descriptions of English grammar in CGEL are perhaps twice as extensive as those of LGSWE.


's (for nouns in English) and the signature NULL.ed.s (or NULL.ed.ing.s) are not assigned to single clusters.

12 in English both nouns and verbs freely occur with the suffixes 9 Baroni (2000) reported success using an MDL-based model in the task of discovering English prefixes.


single top-performing grammar of each generation is preserved, all others are eliminated, and the top-performing grammar is then subjected to mutation.

in a case-by-case fashion, the split between stems and suffixes is modified (in some cases by a shift of a single letter, in others by an unconstrained shift to another location within the word) to form a new grammar.


The central idea of minimum description length analysis (Rissanen 1989) is composed of four parts: first, a model of a set of data assigns a probability distribution to the sample space from which the data is assumed to be drawn; second, the model can then be used to assign a compressed length to the data, using familiar information-theoretic notions; third, the model can itself be assigned a length; and fourth, the optimal analysis of the data is the one for which the sum of the length of the compressed data and the length of the model is the smallest.

we seek a minimally compact specification of both the model and the data, simultaneously.


Let us suppose that we know (part of) the correct analysis of a set of words, and we wish to create a model using that knowledge.

we know which words have no morphological analysis, and for all the words that do have a morphological analysis, we know the final suffix of the word.


The MDL model designed in the preceding section will be of use only if we can provide a practical means of creating one or more plausible morphologies for a given corpus.

we need bootstrapping heuristics that enable us to go from a corpus to such a morphology.


It would also be reasonable, in this case, to analyze the latter stem dcriv as composed of ~cri plus a suffix -v, although in this case, there are no additional benefits to be gained from the more fine-grained analysis.

31 consider a paradigm with a set {j~i} of suffixes.


Here we run straight into the problem of assigning very short strings a lower likelihood of being words than longer strings.

it is difficult to avoid positing a certain number of very short stems, as in English m and an, the first because of pairs such as me and my, the second because of pairs such as an and any, but these facts should not be taken as strong evidence that man is a compound.


In work in progress, we have added to the capability of the algorithm the ability to posit suffixes that are in part subtractive morphemes.

in English, we would like to establish a single signature that combines NULL.ed.ing.s and e.ed.es.ing (for jump and love, respectively).


I have not employed, in the design of this algorithm, a great deal of innate knowledge regarding morphology, but that is for the simple reason that knowledge of how words divide into subpieces is an area of knowledge which no one would take to be innate in any direct fashion: if sanity is parsed as san + ity in one language, it may perfectly well be parsed as sa + nity in another language.

while passion may flame disagreements between partisans of Universal Grammar and partisans of statistically grounded empiricism regarding the task of syntax acquisition, the task which we have studied here is a considerably more humble one, which must in some fashion or other be figured out by grunt work by the language learner.


The result would be the discovery of two grammars of English, G1 and G2, with the following properties: G1 is inherently simpler than G2, using some appropriate notion of Turing machine program complexity, and yet G2 is the correct grammar of English, based on some of the complexity of G2 being the responsibility of linguistic theory, hence "free" in the complexity competition between G1 and G2.

the proponent of the generative view must be willing to acknowledge that overall complexity of the grammar of a language may be greater than logically necessary due to evolution's investment in one particular style of programming language.


Looking at the actual tag precision values (see Table 9 below), we see that the precision is generally more dependent on the tag than on the tagger, so that TagPrecision always tends to select the easier tag.

it uses less specific rather than more specific information.


Although the results reported in this paper are very positive, many directions for research remain to be explored in this area.

we have high expectations for the following two directions.


However, we do have a case study with which to investigate this question, as we have previously performed a 13 1/2-month effort in which we focused on improving performance on the MUC-6 task.

our goal was to see how far the FASTUS paradigm could be pushed by way of making as many incremental improvements as possible.


1° Such methods are nearly always used in conjunction with some form of dynamic programming (henceforth DP).

search efficiency for these parsers is improved by both statistical search heuristics and DP.


This perspective on conditioning production probabilities makes it easy to see that, in essence, by conditioning these probabilities, we are growing the state space.

the number of distinct nonterminals grows to include the composite labels; so does the number of distinct productions in the grammar.


In that case, we want to condition the expansion on how the conjoining constituent expanded.

this attempts to capture a certain amount of parallelism between the expansions of conjoined categories.


Then, under certain assumptions, given a large enough sample, the sample mean of the negative log probability of a model will converge to its cross entropy with the true model.

14 H(p,q) = -lira 1_ logq(w~) r/--+ Oo n where w~ is a string of the language L. In practice, one takes a large sample of the language, and calculates the negative log probability of the sample, normalized by its size.


Note, however, that this renormalization factor is necessarily less than one, and thus would uniformly increase each word's probability under the model, that is, any perplexity results reported below will be higher than the "true" perplexity that would be assigned with a properly normalized distribution.

renormalizing would make our perplexity measure lower still.


One complicating issue has to do with the tokenization in the Penn Treebank versus that in the HUB1 lattices.

contractions (e.g., he' s) are split in the Penn Treebank (he 's) but not in the HUB1 lattices.


So, Shieber's algorithm is sound as a recognizer, 6 but not as a transducer or parser (as in van Noord, [1997]) where the correctness of output models (i.e., parse trees) is critical.

shieber's algorithm is correct up to Tomuro and Lytinen Nonminimal Derivations licensing, but incorrect on the basis of a stronger criteria of minimality.


They demonstrate very well the increasing degrees of precision in which utterances are specified and the associated commitments about possible text portions that express these specifications.

valuable are the comparisons of alternative representations, ranging from skeletal propositions over lexicalized case frames to canned text, including a comparison of the consequences for the distribution of work over the system components involved and discussions of the associated pros and cons.


Although the variety of possible initial representations for NLG is discussed at some length in the book, the associated delicacy does not become clear.

some information in domain model representations may appear only implicitly, and such pieces of information may or may not be required for the functionality of the generation system, depending on subtleties in the intended coverage.


Free indexation assumes that phrases (specifically noun phrases) are freely assigned numerical indices.

a node is taken to be labeled by both a category and a random integer called the index.


He remarks on page 70 that "if descriptive complexity results similar to ours can be established for larger language-theoretic complexity classes (for the indexed languages, for instance) it may be possible to make the argument that GB can be restricted to principles that are definable in the corresponding theory without losing the ability to account for the full class of natural languages."

what has been done for the (strongly) context-free languages might be also possible for larger classes, like the TAG languages or the indexed languages (Rogers [1998] shows that in fact this can be achieved for TAGs).


If we assume that the part of speech of each polyhomographic word in context is known, then 88% of word types would be disambiguated to the homograph level.

(88% do not have two homographs with the same part of speech.)


In order to analyze the effectiveness of our tagger in more detail, we split the main corpus into sub-corpora by grammatical category.

we created four individual sub-corpora containing the ambiguous words which had been partof-speech tagged as nouns, verbs, adjectives, and adverbs.


The other important difference between MBDP-1 and the present model is that MBDP-1 assumes a uniform distribution over all possible word orders.

in a corpus that contains nk distinct words such that the frequency in the corpus of the ith distinct word is given byfk(i), the probability of any one ordering of the words in the corpus is I-ITk ilk(i)!


Brent (1999, 101) suggests that it might be worthwhile to study whether learning phoneme probabilities from distinct lexical entries yields better results than learning these probabilities from the input corpus.

rather than inflating the probability of the phoneme "th" in the by the preponderance of the and the-like words in actual speech, it is better to control it by the number of such distinct words.


In this paper, we focus on argument structure--the thematic roles assigned by a verb to its arguments--as the way in which the relational semantics of the verb is represented at the syntactic level.

our proposal is to automatically classify verbs based on argument structure properties, using statistical corpus-based methods.


To preview our results, we demonstrate that combining just five numerical indicators, automatically extracted from large text corpora, is sufficient to reduce the error rate in this classification task by more than 50% over chance.

we achieve almost 70% accuracy in a task whose baseline (chance) performance is 34%, and whose expert-based upper bound is calculated at 86.5%.


The causative feature was approximated by the following steps, intended to capture the degree to which the subject of a verb can also occur as its object.

for each verb occurrence, the subject and object (if there was one) were extracted from the parsed corpus.


One issue we must address is how precisely the automatic counts reflect the actual linguistic behaviour of the verbs.

we must be assured that the patterns we note in the data in Table 6 are accurate reflections of the differential behaviour of the verb classes, and not an artifact of the way in which we estimate the features, or a result of inaccuracies in the counts.


The numbers reported are the averages of the results over all the trials.

the average accuracy and standard error from each random division of the data (a single cross-validation run including 10 training and test sets) are averaged across the 50 different random divisions.


In conclusion, we do not find that there is a simple mapping from frequency to accuracy.

it is not the case that more frequent classes or verbs are more accurately classified.


Others have tackled the problem of lexical semantic classification, but using only subcategorization frequencies as input data (Lapata and Brew 1999;Schulte im Walde 2000).

these researchers have not explicitly addressed the definition of features to tap directly into thematic role differences that are not reflected in subcategorization distinctions.


We remark, however, that deep linguistic analysis cannot be eliminated--in our approach it is embedded in the selection of the features to count.

our features are derived through a detailed analysis of the differences in thematic role assignments across the verb classes under investigation.


FCA is an applied mathematical discipline based on a formal notion of concepts and concept hierarchies and allowing the exploitation of mathematical reasoning for conceptual data analysis and processing.

fCA permits the efficient construction of dependency lattices that effectively represent the functional and set-valued dependencies established among the domains of some data relation.


For instance, the equality rn(gl) = re(g2) in the lattice of Figure 3 above can also motivate a particular grouping of information in a corresponding linguistic presentation.

whereas graphically the equality motivates an association of both Gropius and Breuer with the graphical attributes allocated to Professions and Schools, textually we may connect both artists in a single sentence: i.e., gl (concerning Gropius) and g2 (concerning Breuer) can be compactly expressed by collapsing their (identical) school and profession attributes: Both Gropius and Breuer were architects and taught at Harvard.


However, the implications of this multi-lingual revolution for computational linguistics beyond the level of word-processing have not been well explored, and Richard Sproat's book A Computational Theory of Writing Systems is a welcome contribution.

sproat's observations and theories are motivated and tested by years of work at AT&T on text-to-speech systems.


He also argues that evaluation of anaphora resolution systems should take into account several factors beyond simple accuracy of resolution.

both developer-oriented (e.g., related to the selection of optimal resolution factors) and application-oriented (e.g., related to the requirement of the application, as in the case of information extraction, where a proper name antecedent is needed) evaluation metrics should be considered.


In her squib, Byron maintains that additional kinds of information should be included in an evaluation in order to make the performance of algorithms on pronoun resolution more transparent.

she suggests that the pronoun coverage be explicitly reported and proposes that the evaluation details be presented in a concise and compact tabular format called standard disclosure.


A fundamental question that needs further investigation is how far the performance of anaphora resolution algorithms can go and what the limitations of knowledge-poor methods are.

more research should be carried out on the factors influencing the performance of these algorithms.


24 Through this preference, cases are accounted for, too, in which appropriate third person antecedents occur outside a passage of quoted speech containing the first person pronoun.

this renders possible the determination of nonpronominal anchors (cf.


More precisely, the above results indicate that the text genre is reflected in some formal properties of the cohesion structure that are important clues for the choice of factors.

the experiments indicate that salience factors and weights should be assigned in a genre-specific way.


The different arguments put forward for an enhancement of the evaluation methodology for coreference resolution systems have been confirmed.

the evaluation results have proven the nonadverbial/matrix and head emphasis, hierarchy of syntactic roles, and cataphora penalty.


It is an important subtask in natural language processing systems.

information extraction (IE) systems like those built in the DARPA Message Understanding Conferences (Chinchor 1998;Sundheim 1995) have revealed that coreference resolution is such a critical component of IE systems that a separate coreference subtask has been defined and evaluated since MUC-6 (MUC-6 1995).


In this paper, we focus on the task of determining coreference relations as defined in MUC-6 (MUC-6 1995) and MUC-7 (MUC-7 1997).

a coreference relation denotes an identity of reference and holds between two textual elements known as markables, which can be definite noun phrases, demonstrative noun phrases, proper names, appositives, sub-noun phrases that act as modifiers, pronouns, and so on.


Our named entity recognition module uses the HMM approach of Bikel, Schwartz, and Weischedel (1999), which learns from a tagged corpus of named entities.

our part-of-speech tagger, noun phrase identification module, and named entity recognition module are all based on HMMs and learn from corpora tagged with parts of speech, noun phrases, and named entities, respectively.


RESOLVE's score is not high compared to scores attained by the rest of the MUC-6 systems.


We may conclude that not only does corpus evidence fail to confirm the canonical ordering, but in fact corpus analysis itself is not sufficient to evaluate the claims of CT without taking into account the underlying semantic content of a text.

statistics about the relative frequency of occurrences of different transition types do not in themselves tell us much about which transitions are preferred in particular situations since they do not take account of the choices available to an author.


2 Our primary goal here is thus to bridge the gap between the grammatical nature of binding constraints and their full integration into grammar processing.

we aim at achieving this in such a way that a lean interface between grammar and reference processing emerges.


Another important problem with the P k metric is that it allows some errors to go unpenalized.

it does not take into account the number of segment boundaries between the two ends of the probe.


There is a slight difference for the case of p = 0.05 because the error probability is too small for some of the trends to manifest themselves.

the differences in the way WD treats the different segmentations disappear when the error probability is this small.


Our summary of the algorithm glosses over many details, yet still allows us to discuss completeness.

we disregard any special provisions that might be made for the selection of head nouns because, arguably, this has to involve realizational issues.


But even within this limited brief, existing algorithms are incomplete.

we have shown Dale and Reiter's (1995) Incremental Algorithm to be intersectively incomplete with respect to Attributes that have overlapping Values and (less surpris-ingly) in some situations where the class of properties is infinitely large.


It proposes that certain limitations on scrambling (reordering of clausal constituents) in German should not be treated as performance limits (in the way the limits on center-embedding in English usually are) because there is a way to make them follow from a grammar formalism (namely, TAGs), and this provides a better explanation than positing a performance restriction stemming from some unknown psychological or neurological basis.

the paper advocates letting the syntactic theory decide: whether a theory is available that will draw a certain syntactic distinction should be a relevant factor in deciding when and whether performance limitations are to be invoked.


An exact translation is probably impossible, for bévue is in all likelihood as different from each of its possible translations as they are from each other.

in general, every translation possibility will omit some nuance or express some other possibly unwanted nuance.


Rather than proliferating conceptual schemata to account for differences between near-synonyms, we will propose that near-synonyms are connected to a single concept, despite their differences in meaning, and are differentiated at a subconceptual level.

the connection of two or more words to the same schema will not imply synonymy but only near-synonymy.


But how can we specify formally just how much similarity of central traits and dissimilarity of peripheral traits is allowed?

just what counts as a central trait and what as a peripheral trait in defining a word?


Second, nuances of meaning, although difficult or impossible to represent in positive, absolute, and context-independent terms, can be represented as differences, in Saussure's sense, between near-synonyms.

every nuance of meaning that a word might have can be thought of as a relation between the word and one or more of its near-synonyms.


They are used to represent non-necessary and indirect aspects of word meaning.

they are concepts that might be implied, suggested, emphasized, or otherwise when a word is used, but not always.


Moreover, since peripheral concepts are defined in the ontology, they can be reasoned about, which, in principle, makes the formalism robust to variation in representation.

if a lexicographer used, say, 'responsibility' to define mistake and 'blameworthiness' to define blunder, the words could still be compared, because inference would find a connection between 'responsibility' and 'blameworthiness'.


So a word's set of distinctions implicitly differentiates the word from its near-synonyms.

if one considers the peripheral concepts, attitudes, styles, and so on, to be dimensions, then the set of distinctions situates a word in a multidimensional space relative to its nearsynonyms.


Comparing two sets of distinctions is not straightforward, however, because, nearsynonyms often differ on seemingly incommensurate dimensions.

the distinctions of one near-synonym will often not match those of another near-synonym, leaving no basis for comparison.


Consider the near-synonyms of forest: Is it possible to decide whether a "large and wild" tract of trees is closer to a "small wild" one or to a "medium-sized non-wild" one?

how much of a change in the size of a forest will compensate for an opposite change in its wildness?


Two separate simultaneous choices might also conflict in their satisfaction of a single preference.

the preference might be satisfied by one choice and negatively satisfied by another choice.


On the other hand, the latter type of decision involves options that might have subtle semantic and stylistic differences but result in the same syntactic structure (though collocational and subcategorization structure can vary).

lexical choice is a two-tiered process that must find both the appropriate set of cluster options and the appropriate set of lexical items (one from each chosen cluster option) whose contributing SemSpec fragments can be unified into a complete well-formed SemSpec.


To avoid an exhaustive search, we use the following heuristic, adopted from Stede (1999): In order to find the globally preferred sentence plan, make the most preferred local choices.

whenever a (local) decision is made between several options, choose the option with the highest score.


The lattice consistency condition is imposed on the set of diacritics to ensure category unity.

9 the syntactic type X can be viewed as an abbreviation for the morphosyntactic type < X where is the universal upper bound.


Note that the problem being addressed here is the engineering problem of estimating predicate argument probabilities, with the aim of producing estimates that will be useful for NLP applications.

we are not addressing the problem of acquiring selectional restrictions in the way this is usually construed (Resnik 1993;Ribas 1995;McCarthy 1997;Li and Abe 1998;Wagner 2000).


The authors argue that trying to recover the interpretation of a sentence directly by means of the grammar is intractable.

they argue that reversing the transformational derivations of the grammar incrementally would simply not be possible, given the recursive complexity of language.


It has to be said that, despite its being couched in terms of modern minimalist linguistic theory, and despite all the attention paid in the book to the full complement of modern models (symbolic, probabilistic, and connectionist), the proposed model has a decidedly "retro" feel to it.

it resurrects notions like the old N-V-N (agent-action-patient) canonical sentence templates (Fodor, Bever, and Garrett 1974), not to mention something closely resembling the transformational theory of grammar, in that the syntactic component is assumed to perform some direct form of reverse transformation operations.


The distributions calculated were simply the empirical distributions from the training data.

occurrences of each role and each set of conditioning events were counted in a table, and probabilities calculated by dividing the counts for each role by the total number Table 4 Sample probabilities for P(r | pt, gov, t) calculated from training data for the verb abduct.


The statistical system for assigning semantic roles described in the previous sections does not fit easily into the chart-parsing framework, as it relies on long-distance dependencies between the target word and its frame elements.

the path feature, which is used to "navigate" through the sentence from the target word to its likely frame elements, may be an arbitrarily long sequence of syntactic constituents.


The centering model keeps track of center continuations and center shifts.


The detailed review of the literature provided in Section 2 revealed that many of the complications and inconsistencies in anaphora resolution start when algorithms are faced with anaphoric elements in complex sentences.

we saw that the interpretation of anaphoric expressions in certain types of clauses would defy any algorithm based on registers of NPs and a uniform lookup mechanism.


We focus on nominalizations whose prenominal modifier is either the underlying subject or direct object of the verb corresponding to the nominalized compound head.

we focus on examples like (3a, 3b) and ignore for the moment nominalizations whose heads correspond to verbs taking prepositional complements (see example (3c)).


Our goal, given a nominalization, is to develop a procedure for inferring whether the modifier stands in a subject or object relation with respect to the head noun.

we need to assign probabilities to the two different relations (subj, obj).


Since we have a choice between two outcomes we will use a likelihood ratio to compare the two relation probabilities (Mosteller and Wallace 1964;Hindle and Rooth 1993).

we will compute the log of the ratio of the probability P(obj | n 1 , n 2 ) to the probability P(subj | n 1 , n 2 ).


The sample was manually inspected, and compounds with modifiers whose relation to the head noun was other than subject or object were discarded.

nominalizations were discarded if: (a) the relation between the head and the modifier was any of the semantic relations listed in (1) (e.g., cause, have, make); these compounds represented 28.0% of the sample; (b) the head was derived from verbs taking prepositional objects (see example (3c)); these nominalizations represented 9.2% of the sample.


The confusion probability P C is an estimate of the probability that a word w 1 can be substituted for a word w 1 , in the sense of being found in the same contexts.

the metric expresses how probable it is for word w 1 to occur in contexts in which word w 1 occurs.


It is totally well-typed if, in addition, every feature F such that Approp(t, F) is defined occurs in A.

a TFS is totally well-typed if it has all and only the features that are appropriate for its type, with appropriate values, and the same holds for all its substructures.


As the XTAG grammar has been developed by as many as a dozen developers, over a period of more than a decade, such errors are probably unavoidable.

a single name is used for two different features or values, with completely different intentions in mind.


The two main negative effects speech disfluencies have on summarization are that they (1) decrease the readability of the summary and (2) increase its noncontent noise.

for informal conversations, the percentage of disfluent words is quite high, typically around 20% of the total words spoken.


(2000), we conjecture that future work in this field will demonstrate the additional benefit of incorporating prosodic information, such as stress, pitch, and intraturn pauses, into the summarization system.

we would expect improved system performance when speech recognition hypotheses are used as input: In that case, the prosodic information could compensate to some extent for incorrect word information.


In this work, we focus on intrinsic evaluation exclusively.

we want to assess how well the summaries preserve the essential information contained in the original texts.


Identities, synonyms, and hypernyms/hyponyms (which together define a tree of "is a" relations between words) are the relations among words that might cause them to be grouped into the same lexical chain.

words may be grouped when: • Two noun instances are identical, and are used in the same sense.


The task of summary sentence decomposition is to deduce whether a summary sentence is constructed by reusing the original text and to identify reused phrases.

the decomposition program needs to answer three questions for a given summary sentence: (1) Is this summary sentence constructed by reusing the text in the original document?


Decomposition of human-written summaries involves analyzing a summary sentence to determine how it is constructed by humans.

we define the summary sentence decomposition problem as follows: Given a human-written summary sentence, a decomposition program needs to answer three questions: (1) Is this summary sentence constructed by reusing the text in the original document?


For example, two document sentences may express the same information (i.e., they are semantic paraphrases), and all human subjects may consider this information important enough to be in the summary, but half of the subjects selected one sentence and half selected the other; thus, both sentences will be included in the extract although they are semantic paraphrases.

this happened in the extract of document ZF109-601-903.


Respondents could tick Yes, Unsure, or No.

all 21 respondents ticked No for a and Yes for E. They disagreed about whether B, C, and D could be considered to know Java; 3 ticked Yes for B, 5 ticked Yes for C, and 13 ticked Yes for D. even among this relatively homogeneous group, there was considerable disagreement over what the phrase knows Java meant in terms of actual knowledge of the Java programming language.


In the linguistic community, Parikh (1994) has suggested that utility theory be applied to word choice.

if we know (1) the probability of a word's being correctly interpreted or misinterpreted and (2) the benefit to the user of correct interpretation and the cost of misinterpretation, then we can compute an overall utility to the user of using the word.


One should not, however, expect from this book a sweeping, definitive exposition of the empirical linguistic paradigm.

anyone seeking an introduction to recent advances in empirical computational linguistics will be disappointed.


By including this probability, it is possible to explicitly describe the fact that for instance the German wordübermorgen produces four English words (the day after tomorrow).

the fertility φ = 0 is used for prepositions or articles that have no direct counterpart in the other language.


In this experiment, we use the conventional dictionary as well.

for the Verbmobil task, with the language pair German-English, we observe that for German as the source language the alignment error rate is much higher than for English as source language.


Defining an upper bound K, however, does have a disadvantage: We lose completeness (see van Deemter [2002]).

the algorithm will fail for objects that can be uniquely described only with K + 1 (or more) edges.


The only hard requirement cost functions have to fulfill is monotonicity.

adding an edge e to a graph G should never result in a graph cheaper than G. Formally: The monotonicity assumption helps reduce the search space, since extensions of subgraphs with a cost greater than the best subgraph found up to that point can safely be ignored.


Another advantage of the way the graphbased algorithm models the list of preferred attributes is that finer-grained distinctions can be made than can with the incremental algorithm.

we are not forced to say that values of the attribute type are always cheaper than values of the attribute color.


First, we assign salience weights to the vertices in the scene graph using the salience weights definition proposed by Krahmer and Theune.

subsequently, in the sketch of the basic algorithm (Figure 6), the set of distractors should be redefined as follows: the distractor set is restricted to those vertices n in the scene graph G that currently are at least as salient as the target object v. For target objects that are linguistically salient, this will typically lead to a reduction of the distractor set.


All of these classifiers can be trained in a supervised manner.

given a sample of training data that provides all of the information that is deemed to be relevant to quantifier scope and the actual scope reading assigned to a sentence, these classifiers will attempt to extract generalizations that can be fruitfully applied in classifying as-yet-unseen examples.


The actual grammar rules and associated probabilities that we use in defining our syntactic module are derived from the WSJ corpus of the Penn Treebank by maximumlikelihood estimation.

for each rule N → φ used in the treebank, we add the rule to the grammar and set its probability to C(N→φ) ψ C(N→ψ) , where C(•) denotes the "count" or a rule (i.e., the number of times it is used in the corpus).


Corpus a scope reading to maximize the probability of the pairing.

are syntax and quantifier scope mutually dependent components of grammar, or can scope relations be "read off of" syntax?


12 Our solution to this search problem is to make the simplifying assumption that the syntactic tree that is used in the optimal set of structures (τ * , χ * ) will always be among the top few trees τ for which P S (τ | w 1−n ) is the greatest.

although we suppose that quantifier scope information is relevant to parsing, we do not suppose that it is so strong a determinant as to completely override syntactic factors.


The set of successor hypotheses Succ by which to extend the partial hypothesis (S, C, j) is computed using the constraints defined by the values for numskip, widthskip, nummove, and widthmove, as explained in the Appendix.

a source position k is discarded for extension if the "window" restrictions are violated.


(1999, Section 3.9), that suggest that the apparent advantage of Knuth's algorithm does not necessarily lead to significantly lower time costs in practice.

consider deduction systems with items associated with spans like, for example, that in Figure 1, in which the span of the consequent of an inference rule is the concatenation of the spans of the antecedents.


No two lexicons are related by a one-to-one correspondence (Abplanalp 1998).

the way words are used to refer to extralinguistic reality varies across languages.


The last decade has seen an increase of formal interest in combining what were previously thought as of being two distinct phenomena: anaphora and presupposition.

what I will refer to as binding and accommodation theory (BAT) (Van der Sandt and Geurts 1991;Van der Sandt 1992;Geurts 1999), in which presuppositional expressions are essentially analyzed as rich anaphora, played an important role here.


It is the boxer who escaped who has an apartment.

the existentially quantified noun phrase a boxer ties together two types of information: ordinary asserted information (namely, that a boxer nearly escaped from an apartment) and presuppositional information (the apartment mentioned in the assertion belongs to the boxer mentioned in the assertion).


Because anaphora are able to operate on an intersentential level, the traditional method of assigning closed formulas to sentences caused problems for discourse processing, and one had to resort to a number of ad hoc techniques for constructing the first-order formulas for natural language discourses.

an appropriate firstorder logic formula for the sentence a woman snorts would be ∃x(WOMaN(x)∧SNORT(x)), but if one continues the discourse with She collapses, there is a need to alter the formula into something of the form: one has to extend the scope of the existential quantifier introduced in the translation of the first sentence to cover elements introduced in the second sentence.


The anaphoric link between she and a woman is established by the condition y = x, and illustrates the role of discourse referents: They introduce discourse entities to which pronouns potentially can refer.

discourse referents are candidate antecedents for future anaphoric reference.


The function r is injective, meaning that distinct argument sequences to r produce distinct values.

the values r assigns to x, y, z, xx, yy, zz, xxx, yyy, zzz, and so on are all different.


3 To realize this more complex message, we may need a linguistic form that cannot be described only by a syntactic phrase marker.

we need to consider document structure as well as syntax.


One aspect of RST, perhaps more presentational than substantial, has led, in our view, to confusion: both in the text of their article and the accompanying diagrams, Mann and Thompson seem to assert that rhetorical relations hold between spans of text, rather than between the meanings of these texts.

they treat rhetorical structure as part of document structure.


Consider, for example, the following excerpt from a patient information leaflet: 9IF you find your condition gets worse during treatment A you may be allergic to the cream D or have a skin infection E .

sTOP UsING THE CREAM B AND TELL YOUR DOCTOR As sOON As POssIBLE C (Betnovate leaflet, Glaxo; from APBI 1997) Careful reading of the text, combined with world knowledge, suggests that the following logical condition holds between the propositional content of A and the pair B and C: IF <condition of patient worsens during treatment> THEN <patient must stop taking cream> AND <patient must tell patient's doctor> ENDIF the rhetorical relation of condition holds between A as satellite and the complex of B and C (joined by a list relation) as nucleus.


We learn additionally that the reason why the patient must carry out the imperative actions of B and C is that he or she may be either allergic to the cream (D) or have a skin infection (E).

there is a causal relation between the complex of B and C (the effect) and complex of D and E (the cause).


But it does not distinguish odd metonymies (see (4)) from acceptable ones: in both cases, paraphrases will be generated, at least in principle (see Section 2.1 for explanation and motivation).

the model does not learn conventional constraints, such as that enjoy must take an event of duration as its argument (Godard and Jayez 1993).


A relatively straightforward approach to the interpretation of (7a) and (8a) would be to extract from the corpus (via parsing) paraphrases in which the additional information (e.g., drinking and watching), which is absent from (7a) and (8a), is fleshed out.

we would like to find in the corpus sentences whose main verb is begin followed either by the progressive VP complement drinking or by the infinitive to drink, selecting for the NP coffee as its object.


We will therefore make the following approximation: The rationale behind this approximation is that the likelihood of seeing a noun o as the object of an event e is largely independent of whether e is the complement of another verb.

v is conditionally independent of e, since the likelihood of o is (largely) determined on the basis of e and not of v. Consider again example (8a): Mary enjoyed the film.


It is important to stress that the probabilistic model outlined above is a model of the interpretation rather than the grammaticality of metonymic expressions.

we do not assume that it can distinguish between well-formed and odd metonymic expressions (see the examples in (4)).


In this way, each subject can establish his own rating scale, thus yielding maximally fine-grained data and avoiding the known problems with the conventional ordinal scales for linguistic data (Bard, Robertson, and Sorace 1996;Cowart 1997;Schütze 1996).

mE does not restrict the range of the responses.


Accordingly, we assume that the argument relation rel is independent of whether the verb e (standing in relation rel with noun n) is modified by an adverb a (see (44)).

it is the verb e and its argument n that determine the relation rel rather than the adjective or adverb a.


The elicited judgments can be further used to derive the grammatical function preferences (i.e., subject or object) for a given adjective.

we can determine the preferred interpretation for individual adjectives on the basis of the human data and then compare these preferences against the ones produced by our model.


Given an adjective-noun combination, we are interested in finding the events most closely associated with the noun modified by the adjective.

we are interested in the verbs whose object or subject is the noun appearing in the adjective-noun combination.


As explained in the previous sections, our model does not take into account the wider context within which an adjective-noun combination is found.

because of the ease with which some adjectives combine with practically any noun, it may be the case that more information (i.e., context) is needed in order to obtain intuitively plausible interpretations for metonymic adjectives.


The experimental analysis demonstrates how hand use correlates tightly with the semantic content of discourse.

the kind of synchrony (antisymmetry or mirror symmetry) is shown to provide cues for discourse segmentation.


Later versions of PTMiner include a final filtering stage to clean the extracted corpus; Nie and Cai (2001) independently used features similar to those described here to eliminate noisy pairs.

they used a file length ratio filter, the proportion of sentences that are aligned to empty (after a sentence alignment algorithm is applied), and a criterion that rewards sentence pairs that contain elements from a bilingual dictionary.


Further, it should normalize for text length.

the score is tsim = log Pr (two-word links in best matching) log Pr (all links in best matching) This score is an application of Lin's (1998) information-theoretic definition of similarity.


Queries submitted to IR systems or search engines are often very short.

the average length of queries submitted to the search engines on the Web is about two words (Jansen et al.


This decomposition of P(T | S) is useful in two ways: first, it makes it possible to ignore P(S) when searching forT; second, it allows us to concentrate our efforts on the lexical aspects of P(S | T), leaving it to P(T) (the "target language model") to take care of syntactic and other language-specific aspects.

in one of the simplest and earliest statistical translation models, iBM's Model 1, it is assumed that P(S | T) can be approximated by a computation that uses only "lexical" probabilities P(s | t) over source and target language words s and t. this model completely disregards the order in which the individual words of S and T appear.


The reliability of a parameter is measured with regard to its contribution to the model's entropy (Foster 2000).

we discard the parameters that least affect the overall probability of the training set.


The marker hypothesis is a universal psycholinguistic constraint which states that natural languages are "marked" for complex syntactic structure at surface form by a closed set of specific lexemes and morphemes.

a basic phrase-level segmentation of an input sentence can be achieved by exploiting a closed list of known marker words to signal the start and end of each segment.


Once the marker hypothesis is applied to 17, it would be marked up as in (18):(18) <DET> The man looks <PREP> at <DET> the woman =⇒ <DET> L' homme regarde <DET> la femme.

the English verb subcategorizes for a PP complement which in this case contains two marker words, whereas the French verb regarder is a straightforward transitive verb.


If we were to apply the marker hypothesis to (20), we would derive (21):(21) <DET> The man likes <DET> the woman =⇒ <DET> La femme plaît <PREP>à <DET> l' homme.

without recourse to a lexicon or information about the relative distribution of words and their translations, we would derive the marker chunks in (22):(22) a.


We take advantage of the assumption that where a chunk contains just one non-marker word in both source and target, these words are translations of each other.

where a marker-headed pair contains just two words, as in (16), for instance, we can extract the word-level translations in 23 using the marker hypothesis method of segmentation, smaller aligned segments can be extracted from the phrasal lexicon without recourse to any detailed parsing techniques or complex co-ocurrence measures.


Instead, we take advantage of the fact that the initial phrasal chunks correspond to rule right-hand sides.

for a rule in the Penn Treebank VP −→ VBG, NP, PP, we are certain (if the annotators have done their job correctly) that the first word in each of the strings corresponding to this right-hand side is a VBG, that is, a present participle.


In our system, in some cases the smallest chunk obtainable via the marker-based segmentation process may be something like 27: 27<DET> the good man : le bon homme In such cases, if our system were confronted with a good man, it would not be able to translate such a phrase, assuming this to be missing from the marker lexicon.

accordingly, we convert examples such as (27) into their generalized equivalents, as in (28): (28) <DET> good man : bon homme where Block (2000) substitutes variables for various words in his templates, we replace certain lexical items with their marker tag.


We would like to incorporate into our model a procedure whereby translation chunks extracted from the phrasal and marker lexicons are more highly regarded than those constructed by inserting words from the word-level lexicon into generalized marker chunks.

we want to allocate a larger portion of the probability space to the phrasal and marker lexicons than to the generalized or wordlevel lexicons.


The test sets were created automatically from words contained in at least one of the systems' knowledge bases, with the proviso that the strings corresponding to the 59 rule types we extracted from the Penn Treebank reflected the frequency bias of these rule types, as far as possible.

we wanted to ensure that strings corresponding to a rule type that was (approximately) twice as frequent as some other rule type occurred (approximately) twice as often in the test sets.


In cases such as these, in which one or more words cannot be translated by our system, partial translations such as those in (40) are output: 40A little girl misplaced a full page =⇒ Une petite fille misplaced une pleine page.

although misplaced was present in the system's database, it was not present in the correct context.


That is, although misplaced was present in the system's database, it was not present in the correct context.

it appeared in the phrasal lexicon, as shown in (41): (41) were misplaced =⇒ ontétéégarés The form required in (40) is a simple past-tense verb, but misplaced appears in (41) only as a passive participle.


If the three on-line MT systems translate the phrases extracted from the Penn-II Treebank in different ways, then combining systems to obtain results for AB, AC, BC, and ABC always involves an increase in the number of translations produced, both for sentences and noun phrases.

if an input string receives a translation via chunks derived from the individual on-line systems, when chunks are combined from different systems, more translations will be output for that input string.


Whereas the greatest number of translations for this NP produced from chunks from any individual on-line system is 14, when translation fragments from all three systems are merged (ABC), 224 translations are produced.

combining systems in this way means that all possible combinations of chunks from the systems are produced: the number of translations generated via AB is much larger than those derived from either A or B, as now chunks from A and B may be combined to produce new translations that could not be generated from the individual knowledge sources.


As a further example, consider the translation of the NP the total at risk a year.

when fragments from systems A and B are combined, the "best" translation is comprised of the chunk combination AAB, that is, the three-chunk combination in (44), with the first two chunks obtained from system A, and the last from system B: the translation of this NP improves when the performance of system AB is Translation quality improves when system databases are seeded with more translation pairs and when more knowledge sources are used: Measuring % translation quality using fragments derived from combinations of on-line MT systems.